Emerging trend of big data analytics in bioinformatics: a literature review

Nagaraj, Kalyan; Sharvani, G.S.; Sridhar, Amulyashree

doi:10.1504/ijbra.2018.10009206

Cited by 8 publications

(3 citation statements)

References 204 publications

(147 reference statements)

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“…MAPMAKER has been applied to the construction of linkage maps in a number of organisms; including the human beings [13]. Mutations [14] in its structures can cause various diseases for this purpose simulation in proteins can reveal many new structures. One of the other techniques introduced for protein unfolding is Steering MD [14].…”

Section: Related Workmentioning

confidence: 99%

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Motif Detection in Cellular Tumor p53 Antigen Protein Sequences by using Bioinformatics Big Data Analytical Techniques

Ali¹,

Yasin²,

Draz³

et al. 2018

ijacsa

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Abstract-Due to the rapid growth of data in the field of big data and bioinformatics, the analysis and management of the data is a very difficult task for the scientist and the researchers. Data exists in many formats like in the form of groups and clusters. The data that exist in the group form and have some repetition patterns called Motifs. A lot of tools and techniques are available in the literature to detect the motifs in different fields like neural networks, antigen/antibody protein, metabolic pathways, DNA/RNA sequences and Protein-Protein Interactions (PPI). In this paper, motif detection is done in tumor antigen protein, namely, cellular tumor antigen p53 (Guardian of the protein and genome) that regulate the cell cycle and suppress the tumor growth in the human body. As tumor is a death causing disease and creates a lot of other diseases in human beings like brain stroke, brain hemorrhage, etc. So there needs to investigate the relation of the tumor protein that prevents the human from not only brain tumor but also from a lot of other diseases that is created from it. To find out the gap between the motifs in the tumor antigen the GLAM2 is used that detects the distance between the motifs very efficiently. Same tumor antigen protein is evaluated at different tools like MEME, TOMTOM, Motif Finder and DREME to analyze the results critically. As tumor protein exists in multiple species, so comparison of homo tumor antigen protein is also done in different species to check the diversity level of this protein. Our purposed approach gives better results and less computational time than other approaches for different types of user characteristics.

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Section: Related Workmentioning

confidence: 99%

“…Mutations [14] in its structures can cause various diseases for this purpose simulation in proteins can reveal many new structures. One of the other techniques introduced for protein unfolding is Steering MD [14]. In [15] analysis is done on the whole genomes to find out the repetitive protein sequences called non-B motifs.…”

Section: Related Workmentioning

confidence: 99%

Motif Detection in Cellular Tumor p53 Antigen Protein Sequences by using Bioinformatics Big Data Analytical Techniques

Ali¹,

Yasin²,

Draz³

et al. 2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…By focusing on the relationships between a small numbers of variables, Principal Component Analysis (PCA) can reveal the most significant shifts in the underlying data and thereby reduce the dimensionality of the whole dataset. As part of the process of predicting pupils' academic performance, principal component analysis is used to simplify the underlying data [27][28] [29][30] [31]. On the described data set, two experiments were conducted to extract features.…”

Section: Feature Extractionmentioning

confidence: 99%

Analysis and Prediction of Student Performance by Using A Hybrid Optimized BFO-ALO Based Approach

Kumar

Ahuja

2022

IJRITCC

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Data mining offers effective solutions for a variety of industries, including education. Research in the subject of education is expanding rapidly because of thebigquantityof student data that can be utilized to uncover valuable learning behavior patterns. This research presents a method for forecasting the academic presentation of students in Portuguese as well as math subjects, and it is describing with the help of 33 attributes. Forecasting the educationalattainment of students is the most popular field of study in the modern period. Previous research has employed a variety of categorization algorithms to forecast student performance. Educational data mining is a topic that needs a lot of research to improve the precision of the classification technique and predict how well students will do in school. In this study, we made a method to predict how well a student will do that uses a mix of optimization techniques. BFO and ALO-based popular optimization techniques were applied to the data set. Python was used to process all the files and conduct a performance comparison analysis. In this study, we compared our model's performance with various existing baseline models and examined the accuracy with which the hybrid algorithm predicted the student data set. To verify the expected classification accuracy, a calculation was performed. The experiment's findings indicate that the BFO-ALO Based hybrid model, which, out of all the methods, with a 94.5 percent success rate, is the preferred choice.

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Data Analytics in Healthcare: A Tertiary Study

et al. 2022

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The field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak monitoring, and clinical decision support have been automated to various degrees. Consequently, the intersection of healthcare and data analytics has received scientific attention to the point of numerous secondary studies. We analyze studies on healthcare data analytics, and provide a wide overview of the subject. This is a tertiary study, i.e., a systematic review of systematic reviews. We identified 45 systematic secondary studies on data analytics applications in different healthcare sectors, including diagnosis and disease profiling, diabetes, Alzheimer’s disease, and sepsis. Machine learning and data mining were the most widely used data analytics techniques in healthcare applications, with a rising trend in popularity. Healthcare data analytics studies often utilize four popular databases in their primary study search, typically select 25–100 primary studies, and the use of research guidelines such as PRISMA is growing. The results may help both data analytics and healthcare researchers towards relevant and timely literature reviews and systematic mappings, and consequently, towards respective empirical studies. In addition, the meta-analysis presents a high-level perspective on prominent data analytics applications in healthcare, indicating the most popular topics in the intersection of data analytics and healthcare, and provides a big picture on a topic that has seen dozens of secondary studies in the last 2 decades.

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Emerging trend of big data analytics in bioinformatics: a literature review

Cited by 8 publications

References 204 publications

Motif Detection in Cellular Tumor p53 Antigen Protein Sequences by using Bioinformatics Big Data Analytical Techniques

Motif Detection in Cellular Tumor p53 Antigen Protein Sequences by using Bioinformatics Big Data Analytical Techniques

Analysis and Prediction of Student Performance by Using A Hybrid Optimized BFO-ALO Based Approach

Data Analytics in Healthcare: A Tertiary Study

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